Atypical Neural Responses to Vocal Anger in Attention Deficit/Hyperactivity Disorder

31 Pages·2017·1.06 MB·English

Article
Atypical neural responses to vocal anger
in attention-deficit/hyperactivity disorder
Chronaki, Georgia, Benikos, Nicholas, Fairchild, Graeme and
Sonuga-Barke, Edmund
Available at http://clok.uclan.ac.uk/14182/
Chronaki, Georgia ORCID: 0000-0001-5146-2510, Benikos, Nicholas, Fairchild,
Graeme and Sonuga-Barke, Edmund (2014) Atypical neural responses to
vocal anger in attention-deficit/hyperactivity disorder. The Journal Of Child
Psychology And Psychiatry And Allied Disciplines, 56 (4). pp. 477-487. ISSN
0021-9630
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Journal of Child Psychology and Psychiatry, Authors’ final draft post-refereeing

Atypical Neural Responses to Vocal Anger in Attention-Deficit/Hyperactivity Disorder

Georgia Chronaki, Ph.D.1,2, Nicholas Benikos 1, Graeme Fairchild Ph.D. 1 and Edmund J. S.
Sonuga-Barke. Ph.D. 1,3

1. Developmental Brain-Behaviour Laboratory, Psychology, University of Southampton,
Southampton, United Kingdom.
2. Neuroscience and Aphasia Research Unit, School of Psychological Sciences,
University of Manchester, Manchester, United Kingdom.
3. Department of Experimental Clinical and Health Psychology, Ghent University, Ghent,
Belgium.

Corresponding Author:
Georgia Chronaki, Ph.D.
Faculty of Medical and Human Sciences
School of Psychological Sciences
Brunswick Street
Manchester M13 9PL
United Kingdom
Phone: [phone]
E-mail: [email protected]

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Key points:
 Responses to expressions of hostility by others may be altered in children with ADHD
because they have difficulties in recognising negative emotions.
 In this, the first study of its kind, we used electrophysiological methods to study brain
event-related potentials of children with ADHD in response to angry, happy and
neutral vocal expressions.
 Compared to controls, children with ADHD displayed significant N100 enhancement
and P300 attenuation to angry relative to neutral voices.
 This pattern of results provides new evidence that very early stages of emotion
processing are implicated in ADHD.
 Future research should test the hypothesis that the N100 component is a marker for
automatic hyper-orientation to vocal threat stimuli in ADHD.
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Abstract
Background: Deficits in facial emotion processing, reported in attention-deficit/hyperactivity
disorder (ADHD), have been linked to both early perceptual and later attentional components
of event-related potentials (ERPs). However, the neural underpinnings of vocal emotion
processing deficits in ADHD have yet to be characterised. Here, we report the first ERP
study of vocal affective prosody processing in ADHD.
Methods: ERPs of six to eleven year old children with ADHD (n=25) and typically
developing controls (n=25) were recorded as they completed a task measuring recognition
of vocal prosodic stimuli (angry, happy and neutral). Audiometric assessments were
conducted to screen for hearing impairments.
Results: Children with ADHD were less accurate than controls at recognising vocal anger.
Relative to controls, they displayed enhanced N100 and attenuated P300 components to
vocal anger. The P300 effect was reduced, but remained significant, after controlling for
N100 effects by rebaselining. Only the N100 effect was significant when children with ADHD
and comorbid conduct disorder (n=10) were excluded.
Conclusion: This study provides the first evidence linking ADHD to atypical neural activity
during the early perceptual stages of vocal anger processing. These effects may reflect pre-
attentive hyper-vigilance to vocal anger in ADHD.

Keywords: attention-deficit/hyperactivity disorder, conduct disorder, emotion processing,
vocal, ERP, prosody.
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Introduction
Studies of attention-deficit/hyperactivity disorder (ADHD) pathophysiology have typically
focused on cognitive (Konrad, Neufang, Hanisch, Fink & Herpertz-Dahlmann, 2006) and,
more recently, motivational processes (Sonuga-Barke & Fairchild, 2012). However, emotion
dysregulation is increasingly regarded as an important clinical feature of the condition (Shaw,
Stringaris, Nigg & Leibenluft, 2014). Consequently there has been a renewed interest in how
individuals with ADHD process negative emotional expressions, such as anger, in the faces
and voices of others (Uekermann et al., 2010). Such expressions of hostility may act as
triggers for negative emotional outbursts in children with ADHD and contribute to coercive
cycles of parent-child interaction (Johnston & Jassy, 2007).
Building on the body of relevant behavioural evidence (Chronaki et al., 2013; Shapiro,
Hughes, August & Bloomquist, 1993), recent electrophysiological research has
demonstrated altered neural responses to facial expressions of anger in ADHD. Both early
sensory and later attention-mediated cognitive processes have been implicated (Dennis,
Malone & Chen, 2009; Eimer & Holmes, 2007). For example, Williams and colleagues found
enhanced facial anger-related modulation of the N170 event-related potential (ERP)
component followed by an attenuated P300 in adolescents with ADHD (Williams et al., 2008).
These effects were interpreted as indicating deficits in both early perceptual and later
context-related processing of angry faces (Williams et al., 2008). In contrast to the above
findings, Ibáñez and colleagues (2011) found that controls showed modulation of the N170
component by valence but this pattern was not observed in adult ADHD patients in a task
that involved classifying the valence of facial expressions. In addition, both child (Köchel,
Leutgeb & Schienle, 2014) and adult ADHD samples (Köchel, Leutgeb & Schienle, 2012)
have been reported to show attenuated late positive potentials during the processing of
angry faces in an inhibitory task.
Emotional meaning in the expressions of significant others in our social environment
is not solely conveyed by facial expressions, but also by voices – both in terms of content
and also prosody, especially emotional tone (Banse & Scherer, 1996). While ADHD-related
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deficits in emotion prosody recognition have been identified in behavioural studies (Chronaki
et al., 2013; Corbett & Glidden, 2000; Norvilitis, Casey, Brooklier & Bonello, 2000), there
have been no studies investigating their neural underpinnings. This is surprising given that in
everyday life vocal anger is likely to be as critical as facial anger in terms of triggering
inappropriate emotional responses in individuals with ADHD.
In healthy controls, vocal emotional expressions modulate ERPs over a range of scalp
regions in both adult (Schirmer & Kotz, 2006) and child samples (Chronaki et al., 2012). In a
similar way to that seen in face processing studies, there are emotion specific effects at both
early sensory and later attention-mediated components (Iredale, Rushby, McDonald,
Dimoska-Di Marco & Swift, 2013; Schirmer & Kotz, 2006). For instance, the N100, an index
of the initial extraction of information and sensory analysis of stimuli (Wunderlich & Cone-
Wesson, 2001), has been shown to be affected by vocal emotional content (Iredale et al.,
2013; Pinheiro et al., 2011). The auditory N100 (80-150 ms) shows a parietal distribution in
young children which displays an anterior shift in the teenage years (Pang & Taylor, 2000).
The N100 response to vocal anger, in particular, is prominent in parietal-occipital areas in 6-
11-year-old children (Chronaki et al., 2012). The P300, reflecting attentional engagement
and allocation of cognitive resources (Banaschewski & Brandeis, 2007; Nelson & McCleery,
2008), and the N400, reflecting cognitive evaluation (Schirmer & Kotz, 2006), are also
modulated by emotion, especially for negatively-valenced stimuli (Broyd et al., 2012). The
N400 to vocal emotion is evident in parietal-occipital scalp regions in healthy individuals
(Toivonen & Rama, 2009). Research using Functional Magnetic Resonance Imaging (fMRI)
has shown activation of the cuneus in medial occipital cortex in response to angry voices
(Sander et al., 2005), which was suggested to reflect vivid visual mental imagery induced by
hearing angry voices (Kosslyn & Thompson, 2003).
This paper reports the first ERP study of vocal prosody recognition in ADHD. Building on
previous findings, we hypothesized that anger prosody recognition deficits observed
behaviourally in ADHD would be reflected in ERP abnormalities, and specifically in
attenuated P300 amplitudes to vocal anger. In addition, building on the face processing
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literature showing exaggerated early perceptual face-specific components (i.e., N170) to
anger, we expected increased N100 amplitudes to anger from vocal expressions in ADHD.
The above would fit with a model of rapid initial hyper-orientation to threatening (angry)
stimuli followed by deficits in later, cognitive evaluation of the emotional significance of these
stimuli (Williams et al., 2008). As vocal signals of anger normally act as signals of social
punishment, lower sensitivity to anger in children with ADHD at later, more evaluative stages
of processing, may limit their ability to appropriately modify their behaviour in social
interactions.
To test the above hypotheses we examined ERP differences between ADHD and
controls in components related to early sensory processing (N100), later attentional
engagement (P300) and cognitive evaluation (N400) in response to angry, happy and
neutral prosodic stimuli. ADHD is frequently comorbid with conduct disorder (CD;
(Biederman, 2005), and emotional dysregulation (Stringaris, Rowe & Maughan, 2012) and
alterations in emotion recognition are also reported in CD (Fairchild, Van Goozen, Calder,
Stollery & Goodyer, 2009). It is possible that difficulties in emotion processing in ADHD could
be driven by co-occurring CD. Therefore, and contrary to previous ERP research which has
not taken CD comorbidity in account (Williams et al., 2008), we sought to investigate
whether CD comorbidity contributed to our behavioural and ERP results.

Methods and Materials
Participants
Thirty typically-developing children (2 girls) and 36 children with ADHD (1 girl), aged
between 6 and 11 years, were recruited into the study. Informed written consent was
obtained from the parent(s) and written assent from the child. Children with ADHD were
recruited from local child and adolescent mental health clinics and all had a clinical diagnosis
of ADHD. They all undertook a comprehensive clinical research assessment as part of the
South Hampshire ADHD Register. This included the ADHD, CD and Oppositional Defiant
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Disorder (ODD) scales of the parent version of the Diagnostic Interview Schedule for
Children- NIMH (DISC-IV; Shaffer et al., 1993), the parent and teacher version of the
Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997) to establish the
pervasiveness of the presentation and the ‘sadness’ scale of the Children’s Behaviour
Questionnaire (CBQ; Putman & Rothbart, 2006) to provide a measure of internalising
problems. Full scale IQ was also assessed using the Wechsler Intelligence Scale for
Children (WISC-IV; Wechsler, 2004). Patients were only included if they met criteria for
ADHD on the DISC-IV. In addition, all but two scored above borderline thresholds on the
hyperactivity subscales of the SDQ-teacher report. Results did not change after excluding
these two patients. Controls were recruited from local mainstream schools. They completed
the same measures as the ADHD patients apart from the DISC-IV. They also only completed
the Block Design and Vocabulary sub-tests of the WISC-IV. General exclusion criteria were;
a) IQ<75; b) hearing difficulties, as assessed with a clinical audiometer; and c) diagnosis of
autism spectrum disorder or a neurological condition. In addition, two control children were
excluded as they scored above borderline thresholds on the hyperactivity subscales of the
SDQ. Nine children (six ADHD, three controls) were excluded due to excessive ERP artifacts.
The final sample included 25 controls (mean age=9.04 years, SD=1.46 years, age range
6.83-11.67) and 25 children with ADHD (mean age=8.85 years, SD=1.47 years, age range
6.33-11.50). Ten children with ADHD also had a DISC-IV CD diagnosis and ten were taking
methylphenidate medication but all were asked to withdraw their medication 24 hours prior to
testing (5 half-lives). Participant characteristics are presented in Table 1. The study was
approved by the University of Southampton Ethics Committee and the National Health
Service (NHS) Research Ethics Committee.

Vocal Emotion Recognition Task
Pure tone audiometric testing was conducted at the beginning of the experimental session
with a standard clinical audiometer to establish whether participants’ hearing threshold was
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within the average range defined as 25 dB following the British Society of Audiology
Recommended Procedures (2004).Task stimuli consisted of standardised and previously
validated in children (Chronaki et al., 2012) vocal prosodic stimuli (an interjection ‘ah’ sound)
developed by Maurage and colleagues (Maurage, Joassin, Philippot & Campanella, 2007).
These were angry and happy emotions (both at high intensity) along with neutral control
stimuli (Maurage et al., 2007). Vocal stimuli were the same as in our previous work (see
Table S1, Chronaki et al., 2012). To rule out stimulus-specific variation in the ERP
components, we also used two different versions of each vocal stimulus that were recorded
by different female actors. Children were instructed to identify the type of emotional prosody
by pressing one of the three response box buttons with the labels ‘angry’, ‘happy’ or ‘neutral’.
Each trial began with the presentation of a central fixation cross (500 ms), which was
replaced by a blank screen and the simultaneous presentation of the prosodic stimulus. The
screen remained blank until the participants responded and there was a 1000 ms interval
before the onset of the next trial. Button presses were logged via Presentation software
(Neurobehavioral Systems, Albany, CA). The session consisted of a practice block (12 trials
- four of each emotion) and 360 experimental trials (120 trials per emotion type/60 trials per
actor) were presented in two blocks of 180 trials. Emotional stimulus presentation order was
randomized. There was a 5-minute rest between blocks. The task lasted approximately one
hour. Children did not receive any feedback about their responses.

Electrophysiological recording and processing
Electroencephalographic (EEG) data were recorded from an electrode cap (Easycap,
Herrsching, Germany) containing 66 equidistant silver/silver chloride (Ag/AgCl) electrodes
using Neuroscan Synamps2 70 channel EEG system. Cap electrodes were referenced to the
nose. The EEG data were sampled at 250 Hz with a band pass filter at 0.1 to 70 Hz using an
AC procedure and recorded from 19 sites (see Figure S1). A ground electrode was fitted
midway between the electrode at the vertex and frontal site (number 32). Vertical electro-
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oculogram (vEOG) was recorded from four electrodes: two bipolar electrodes were placed
directly beneath the left and right eyes, while the two electrodes placed above the right and
left eye were included within the electrode cap. Impedances for vEOG, reference and cap
electrodes were kept below 5 kΩ. The ERP epoch was defined as 100 ms pre-stimulus to
1000 ms post-stimulus and was filtered with a low-pass filter down 48 dB at 32 Hz. An ocular
artifact reduction procedure (Semlitsch, Anderer, Schuster & Presslich, 1986) based on
vEOG activity was used to remove the influence of blinks and other eye movements; epochs
were rejected if amplitudes exceeded ±150 μV at any EOG or scalp sites included in
analyses or if participants responded incorrectly. Average ERPs were calculated for each
emotion type (Angry, Happy, Neutral).
A minimum of 20 artifact-free epochs out of a total of 60 epochs for each emotion
type (and a minimum of 10 artifact free epochs per actor) were used for calculating ERP
averages for each block. The mean and SD of the number of epochs included in the
analyses for each emotion type were as follows: Controls: Angry: M=48.64, SD=7.61,
Neutral: M=49.50, SD=5.44, Happy: M=46.80, SD=8.14; ADHD group: Angry: M=40.90,
SD=11.75, Neutral: M=39.80, SD=12.94, Happy: M=38.30, SD=12.90. There was no
significant effect of emotion condition or actor on the number of correct and artifact free
epochs in the healthy controls and ADHD group (ps >.17). The groups differed in the number
of correct and artifact free epochs for angry (F(1,48)=7.60,p<.01), happy(F(1,48)=7.80,p<.05)
and neutral stimuli (F(1,48)=11.90,p<.01), with fewer artifact-free epochs for children with
ADHD than controls. However, mean amplitude is not biased by the mean number of trials
(Luck, 2010). Grand average ERP waveforms were displayed for each stimulus for the
purpose of defining each component’s latency range. A baseline-to-peak mean amplitude
method was used to calculate the N100 (80-180 ms), P300 (260-380 ms) and N400 (380-
500 ms) components. Peaks were confirmed by visual inspection and clearly visible in all
individual waveforms. In line with our earlier work (Chronaki et al., 2012) and previous
literature (Schirmer & Kotz, 2006), the ERP analyses focused on parietal (sites 12, 13, 14,
24, 26) and occipital (sites 37, 38, 39, 40) regions. To increase measurement reliability (Dien
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